Posterior probability represents the prediction market’s newest estimate of how likely an event is to occur. As traders react to information—news, updates, leaks, or expert commentary—they adjust their positions. These trades shift the market probability, creating a posterior belief that replaces the previous one.
This concept mirrors Bayesian reasoning, where a prior probability is updated with new evidence. In prediction markets, the “evidence” arrives through trader behavior. Each buy or sell reflects how participants interpret new information, and the market aggregates these inputs into a single posterior probability. This process is visible in the prediction markets data as a constantly evolving probability curve.
Posterior probabilities help analysts understand how beliefs change over time. They reveal which developments had the biggest impact, how quickly traders reacted, and whether the market became more confident or uncertain. Over many events, posterior probabilities form the backbone of forecasting analysis.
Posterior probability shows the most current crowd expectation after incorporating fresh information. It turns prediction markets data into a real-time forecasting tool that updates as events unfold.
Prediction markets rely on posterior probabilities because events evolve, and forecasts must evolve with them. A static probability loses relevance quickly. Posterior probabilities reflect how traders adjust their beliefs in response to new evidence, making the forecast timely and accurate. This constant refinement produces prediction markets data that is far more informative than traditional snapshots or surveys.
Posterior probabilities form through trading. When new information appears, traders reassess their beliefs and adjust their positions. Their buy and sell actions move the market probability, creating a new posterior estimate. This happens repeatedly as additional information arrives. The entire sequence is captured in the prediction markets data, showing a clear trail of how beliefs shifted.
Analysts can see which events triggered major updates, how strongly the market reacted, and whether belief changes were gradual or sudden. They can compare posterior probabilities with final outcomes to evaluate forecasting accuracy. Posterior data also highlights uncertainty levels at different stages of the event. These insights make prediction markets data essential for understanding market efficiency and information flow.
A prediction market tracks whether an Oscar nominee will win Best Supporting Actress. After a major critics’ award is announced, traders quickly buy shares, pushing the probability higher. This new market probability becomes the posterior—an updated belief reflecting the fresh evidence.
Posterior probabilities depend on accurate, time-stamped updates that show how expectations shift after each new signal. FinFeed's Prediction Markets API provides structured prediction markets data—including probability paths and event outcomes—allowing developers to analyze posterior updates, model belief changes, and build tools that visualize evolving forecasts.
